---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Asian_Pigeonwings_(Clitoria Ternatea)
          '1': Bilayat_(Mexicana_Argemone)
          '2': Choti_dudhi_(Euphorbia_hirta)
          '3': Digitaria_SP_(Digitaria Sanguinalis )
          '4': Dwarf_cassia_(Chamaecrista pumila)
          '5': Gajar_gavat_(Parthenium hysterophorus)
          '6': Graceful_Sandmart_(Euphorbia hypericifolia)
          '7': Harali_(Cynodon_dactylon)
          '8': Kena_(Commplina_benghalensio)
          '9': Lamber_Quarter_plant(Chenopodium )
          '10': Lavhala_(Cyperus_Rotundus)
          '11': Little_Mallow(Malva parviflora)
          '12': Moti_dudhi(Euphorbia_geneculata_L)
          '13': Obscure_morning _glory(Ipomoea obscura)
          '14': Punarnava _(Boerhaavia diffusa)
          '15': Sicklepod_(Senna obtusifolia)
  splits:
  - name: train
    num_bytes: 1741357787
    num_examples: 19141
  download_size: 2161957353
  dataset_size: 1741357787
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- 10K<n<100K
---

# MH Weed16 Weed Variety Classification

A dataset for variety classification of weeds. The dataset contains 19,141 images across 16 classes:  
Images per class:
- Asian_Pigeonwings_(Clitoria Ternatea): 678
- Bilayat_(Mexicana_Argemone): 859
- Choti_dudhi_(Euphorbia_hirta): 580
- Digitaria_SP_(Digitaria Sanguinalis ): 1,561
- Dwarf_cassia_(Chamaecrista pumila): 1,306
- Gajar_gavat_(Parthenium hysterophorus): 1,543
- Graceful_Sandmart_(Euphorbia hypericifolia): 262
- Harali_(Cynodon_dactylon): 709
- Kena_(Commplina_benghalensio): 1,704
- Lamber_Quarter_plant(Chenopodium ): 836
- Lavhala_(Cyperus_Rotundus): 1,177
- Little_Mallow(Malva parviflora): 2,100
- Moti_dudhi(Euphorbia_geneculata_L): 3,002
- Obscure_morning _glory(Ipomoea obscura): 1,637
- Punarnava _(Boerhaavia diffusa): 544
- Sicklepod_(Senna obtusifolia): 643

This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.

## Citation

```bibtex
@article{shinde2025indian,
  title={An Indian annotated weed dataset for computer vision tasks in precision farming},
  author={Shinde, Sayali and Attar, Vahida},
  journal={Data in Brief},
  volume={61},
  pages={111691},
  year={2025},
  publisher={Elsevier}
}
```

Shinde, Sayali; Attar, Dr. Vahida; Technological University Pune, COEP; Technology Innovation Hub, Indian Statistical Institute Kolkata, IDEAS (2025), “MH-Weed16:An Indian Multiclass Annotated Weed Dataset for Computer Vision Tasks ”, Mendeley Data, V2, doi: 10.17632/d3n3mgjjbv.2